[hadoop] hadoop 运行 wordcount

讲准备好的文本文件放到hdfs中

执行 hadoop 安装包中的例子

[root@hadoop01 mapreduce]# hadoop jar hadoop-mapreduce-examples-2.8.0.jar  wordcount /input/ /output/wordcount
17/05/14 02:01:17 INFO client.RMProxy: Connecting to ResourceManager at hadoop01/172.16.253.128:8032
17/05/14 02:01:19 INFO input.FileInputFormat: Total input files to process : 2
17/05/14 02:01:19 INFO mapreduce.JobSubmitter: number of splits:2
17/05/14 02:01:19 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494742494825_0002
17/05/14 02:01:20 INFO impl.YarnClientImpl: Submitted application application_1494742494825_0002
17/05/14 02:01:20 INFO mapreduce.Job: The url to track the job: http://hadoop01:8088/proxy/application_1494742494825_0002/
17/05/14 02:01:20 INFO mapreduce.Job: Running job: job_1494742494825_0002
17/05/14 02:01:35 INFO mapreduce.Job: Job job_1494742494825_0002 running in uber mode : false
17/05/14 02:01:35 INFO mapreduce.Job:  map 0% reduce 0%
17/05/14 02:02:48 INFO mapreduce.Job:  map 100% reduce 0%
17/05/14 02:03:22 INFO mapreduce.Job:  map 100% reduce 100%
17/05/14 02:03:25 INFO mapreduce.Job: Job job_1494742494825_0002 completed successfully
17/05/14 02:03:28 INFO mapreduce.Job: Counters: 49
        File System Counters
                FILE: Number of bytes read=350
                FILE: Number of bytes written=408885
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=584
                HDFS: Number of bytes written=145
                HDFS: Number of read operations=9
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters
                Launched map tasks=2
                Launched reduce tasks=1
                Data-local map tasks=2
                Total time spent by all maps in occupied slots (ms)=145615
                Total time spent by all reduces in occupied slots (ms)=17369
                Total time spent by all map tasks (ms)=145615
                Total time spent by all reduce tasks (ms)=17369
                Total vcore-milliseconds taken by all map tasks=145615
                Total vcore-milliseconds taken by all reduce tasks=17369
                Total megabyte-milliseconds taken by all map tasks=149109760
                Total megabyte-milliseconds taken by all reduce tasks=17785856
        Map-Reduce Framework
                Map input records=14
                Map output records=70
                Map output bytes=666
                Map output materialized bytes=356
                Input split bytes=196
                Combine input records=70
                Combine output records=30
                Reduce input groups=19
                Reduce shuffle bytes=356
                Reduce input records=30
                Reduce output records=19
                Spilled Records=60
                Shuffled Maps =2
                Failed Shuffles=0
                Merged Map outputs=2
                GC time elapsed (ms)=9667
                CPU time spent (ms)=3210
                Physical memory (bytes) snapshot=330969088
                Virtual memory (bytes) snapshot=6192197632
                Total committed heap usage (bytes)=259284992
        Shuffle Errors
                BAD_ID=0
                CONNECTION=0
                IO_ERROR=0
                WRONG_LENGTH=0
                WRONG_MAP=0
                WRONG_REDUCE=0
        File Input Format Counters
                Bytes Read=388
        File Output Format Counters
                Bytes Written=145

　　

查看执行结果：

自定义wordcount :

package com.xwolf.hadoop.mapreduce;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;
import java.util.Arrays;

/**
 * @author xwolf
 * @date 2017-05-14 10:42
 * @since 1.8
 */
public class WordCount {

   static class WordCountMapper extends Mapper<LongWritable,Text,Text,IntWritable> {

        /**
         * map方法的生命周期：  框架每传一行数据就被调用一次
         * @param key   这一行的起始点在文件中的偏移量
         * @param value 这一行的内容
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //读取当前行数据
            String line = value.toString();
            //将这一行切分出各个单词
            String[] words = line.split(" ");
            //遍历数组，输出格式<单词，1>
            Arrays.stream(words).forEach(e -> {
                try {
                    context.write(new Text(e), new IntWritable(1));
                } catch (Exception e1) {
                    e1.printStackTrace();
                }
            });

        }
    }

   static class WordCountReduce extends Reducer<Text,IntWritable,Text,IntWritable>{
        /**
         * 生命周期：框架每传递进来一个kv 组，reduce方法被调用一次
         * @param key
         * @param values
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            //定义一个计数器
            int count = 0;
            //遍历这一组<k,v>的所有v，累加到count中
            for(IntWritable value:values){
                count += value.get();
            }
            context.write(key, new IntWritable(count));

        }
    }

    public static void main(String[] args) throws  Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        //指定job 的jar
        job.setJarByClass(WordCount.class);
        //指定map 类
        job.setMapperClass(WordCountMapper.class);
        //指定reduce 类
        job.setReducerClass(WordCountReduce.class);
        //设置Mapper类的输出key和value的数据类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        //设置Reducer类的输出key和value的数据类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        //指定要处理的数据所在的位置
        FileInputFormat.addInputPath(job, new Path(args[0]));
        //指定处理完成后的数据存放位置
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true)?0:1);
    }
}

　　

打包上传至hadoop 集群。

运行出错

出错

执行mapreduce出现的错，原因是map类和reduce没有加static修饰，因为hadoop在调用map和reduce类时采用的反射调用，内部类不是静态的，没有获取到内部类的实例